{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "\n",
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Introduction to Data Analytics and Geostatistics and Spatial Data Analytics Courses\n",
    "\n",
    "\n",
    "## GeostatsPy: Spatial Continuity Directions for Subsurface Data Analytics in Python \n",
    "\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "\n",
    "### PGE 383 Exercise: Methods to Detect Directions of Continuity with GeostatsPy\n",
    "\n",
    "Here's a simple workflow on detecting the major spatial continuity directions in a spatial dataset with variogram analysis. This information is essential to optimum well placement and prectiction away from wells.  First let's explain the concept of spatial continuity and the variogram.\n",
    "\n",
    "#### Spatial Continuity \n",
    "\n",
    "**Spatial Continuity** is the correlation between values over distance.\n",
    "\n",
    "* No spatial continuity – no correlation between values over distance, random values at each location in space regardless of separation distance.\n",
    "\n",
    "* Homogenous phenomenon have perfect spatial continuity, since all values as the same (or very similar) they are correlated. \n",
    "\n",
    "We need a statistic to quantify spatial continuity! A convenient method is the Semivariogram.\n",
    "\n",
    "#### The Semivariogram\n",
    "\n",
    "Function of difference over distance.\n",
    "\n",
    "* The expected (average) squared difference between values separated by a lag distance vector (distance and direction), $h$:\n",
    "\n",
    "\\begin{equation}\n",
    "\\gamma(\\bf{h}) = \\frac{1}{2 N(\\bf{h})} \\sum^{N(\\bf{h})}_{\\alpha=1} (z(\\bf{u}_\\alpha) - z(\\bf{u}_\\alpha + \\bf{h}))^2  \n",
    "\\end{equation}\n",
    "\n",
    "where $z(\\bf{u}_\\alpha)$ and $z(\\bf{u}_\\alpha + \\bf{h})$ are the spatial sample values at tail and head locations of the lag vector respectively.\n",
    "\n",
    "* Calculated over a suite of lag distances to obtain a continuous function.\n",
    "\n",
    "* the $\\frac{1}{2}$ term converts a variogram into a semivariogram, but in practice the term variogram is used instead of semivariogram.\n",
    "* We prefer the semivariogram because it relates directly to the covariance function, $C_x(\\bf{h})$ and univariate variance, $\\sigma^2_x$:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "Note the correlogram is related to the covariance function as:\n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\frac{C_x(\\bf{h})}{\\sigma^2_x}\n",
    "\\end{equation}\n",
    "\n",
    "The correlogram provides of function of the $\\bf{h}-\\bf{h}$ scatter plot correlation vs. lag offset $\\bf{h}$.  \n",
    "\n",
    "\\begin{equation}\n",
    "-1.0 \\le \\rho_x(\\bf{h}) \\le 1.0\n",
    "\\end{equation}\n",
    "\n",
    "#### Variogram Observations\n",
    "\n",
    "The following are common observations for variograms that should assist with their practical use.\n",
    "\n",
    "##### Observation \\#1 - As distance increases, variability increase (in general).\n",
    "\n",
    "This is common since in general, over greater distance offsets, there is often more difference between the head and tail samples.\n",
    "\n",
    "In some cases, such as with spatial cyclicity of the hole effect variogram model the variogram may have negative slope over somelag distance intervals\n",
    "\n",
    "Negative slopes at lag distances greater than half the data extent are often caused by too few pairs for a reliable variogram calculation\n",
    "\n",
    "##### Observation \\#2 - Calculated with over all possible pairs separated by lag vector, $\\bf{𝐡}$.\n",
    "\n",
    "We scan through the entire data set, searching for all possible pair combinations with all other data.  We then calculate the variogram as one half the expectation of squared difference between all pairs.\n",
    "\n",
    "More pairs results in a more reliable measure.\n",
    "\n",
    "##### Observation \\#3 - Need to plot the sill to know the degree of correlation.\n",
    "\n",
    "**Sill** is the variance, $\\sigma^2_x$\n",
    "\n",
    "Given stationarity of the variance, $\\sigma^2_x$, and variogram $\\gamma(\\bf{h})$:\n",
    "\n",
    "we can define the covariance function:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "The covariance measure is a measure of similarity over distance (the mirror image of the variogram as shown by the equation above).\n",
    "\n",
    "Given a standardized distribution $\\sigma^2_x = 1.0$, the covariance, $C_x(\\bf{h})$, is equal to the correlogram, $\\rho_x(\\bf{h})$: \n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "##### Observation \\#4 - The lag distance at which the variogram reaches the sill is know as the range.\n",
    "\n",
    "At the range, knowing the data value at the tail location provides no information about a value at the head location of the lag distance vector.\n",
    "\n",
    "##### Observation \\#5 - The nugget effect, a discontinuity at the origin\n",
    "\n",
    "Sometimes there is a discontinuity in the variogram at distances less than the minimum data spacing.  This is known as **nugget effect**.\n",
    "\n",
    "The ratio of nugget / sill, is known as relative nugget effect (%). Modeled as a discontinuity with no correlation structure that at lags, $h \\gt \\epsilon$, an infinitesimal lag distance, and perfect correlation at $\\bf{h} = 0$.\n",
    "Caution when including nuggect effect in the variogram model as measurement error, mixing populations cause apparent nugget effect\n",
    "\n",
    "This exercise demonstrates the semivariogram calculation with GeostatsPy. The steps include:\n",
    "\n",
    "1. generate a 2D model with sequential Gaussian simulation\n",
    "2. sample from the simulation\n",
    "3. calculate and visualize experimental semivariograms\n",
    "\n",
    "#### Detecting Directions of Continuity\n",
    "\n",
    "Spatial continuity can be described with nested spatial continuity models:\n",
    "\n",
    "\\begin{equation}\n",
    "\\Gamma_x(\\bf{h}) = \\sum_{i=1}^{nst} \\gamma_i(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "where $\\Gamma_x(\\bf{h})$ is the nested variogram model resulting from the summation of $nst$ nested variograms  $\\gamma_i(\\bf{h})$.\n",
    "\n",
    "Each one of these variogram structures, $\\gamma_i(\\bf{h})$, is based on a geometric anisotropy model parameterized by the orientation and range in the major and minor directions.  In 2D this is simply an azimuth and ranges, $azi$, $a_{maj}$ and $a_{min}$. Note, the range in the minor direction (orthogonal to the major direction.\n",
    "\n",
    "The geometric anisotropy model assumes that the range in all off-diagonal directions is based on an ellipse with the major and minor axes alligned with and set to the major and minor for the variogram.\n",
    "\n",
    "\\begin{equation}\n",
    "\\bf{h}_i = \\sqrt{\\left(\\frac{r_{maj}}{a_{maj_i}}\\right)^2 + \\left(\\frac{r_{maj}}{a_{maj_i}}\\right)^2}  \n",
    "\\end{equation}\n",
    "\n",
    "Therefore, if we know the major direction, range in major and minor directions, we may completely describe each nested componnent of the complete spatial continuity of the variable of interest, $i = 1,\\dots,nst$.\n",
    "\n",
    "In this workflow we will explore methods to detect directionality from a spatial dataset.\n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - [sample_data.csv](https://git.io/fh0CW) at https://git.io/fh0CW.\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB                       # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats                 # GSLIB methods convert to Python    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os                                               # to set current working directory \n",
    "import numpy as np                                      # arrays and matrix math\n",
    "import pandas as pd                                     # DataFrames\n",
    "import matplotlib.pyplot as plt                         # plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see above) GSLIB executables in this directory or a location identified in the environmental variable *Path*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"d:/PGE383\")                                   # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
       "      <td>0.115359</td>\n",
       "      <td>5.736104</td>\n",
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       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "      <td>0.136425</td>\n",
       "      <td>17.211462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.135810</td>\n",
       "      <td>43.724752</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>0.094414</td>\n",
       "      <td>1.609942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.113049</td>\n",
       "      <td>10.886001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     X    Y  Facies  Porosity       Perm\n",
       "0  100  900       1  0.115359   5.736104\n",
       "1  100  800       1  0.136425  17.211462\n",
       "2  100  600       1  0.135810  43.724752\n",
       "3  100  500       0  0.094414   1.609942\n",
       "4  100  100       0  0.113049  10.886001"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"sample_data_biased.csv\")                     # read a .csv file in as a DataFrame\n",
    "#print(df.iloc[0:5,:])                                  # display first 4 samples in the table as a preview\n",
    "df.head()                                               # we could also use this command for a table preview "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will work with all facies pooled together. I wanted to simplify this workflow and focus more on spatial continuity direction detection. Finally, by not using facies we do have more samples to support our statistical inference. Most often facies are essential in the subsurface model. Don't worry we will check if this is reasonable in a bit.   \n",
    "\n",
    "You are welcome to repeat this workflow on a by-facies basis.  The following code could be used to build DataFrames ('df_sand' and 'df_shale') for each facies.\n",
    "\n",
    "```p\n",
    "df_sand = pd.DataFrame.copy(df[df['Facies'] == 1]).reset_index()  # copy only 'Facies' = sand records\n",
    "df_shale = pd.DataFrame.copy(df[df['Facies'] == 0]).reset_index() # copy only 'Facies' = shale records\n",
    "```\n",
    "\n",
    "Let's look at summary statistics for all facies combined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
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       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>289.0</td>\n",
       "      <td>475.813149</td>\n",
       "      <td>254.277530</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>430.000000</td>\n",
       "      <td>670.000000</td>\n",
       "      <td>990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>289.0</td>\n",
       "      <td>529.692042</td>\n",
       "      <td>300.895374</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>549.000000</td>\n",
       "      <td>819.000000</td>\n",
       "      <td>999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>289.0</td>\n",
       "      <td>0.813149</td>\n",
       "      <td>0.390468</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>289.0</td>\n",
       "      <td>0.134744</td>\n",
       "      <td>0.037745</td>\n",
       "      <td>0.058548</td>\n",
       "      <td>0.106318</td>\n",
       "      <td>0.126167</td>\n",
       "      <td>0.154220</td>\n",
       "      <td>0.228790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>289.0</td>\n",
       "      <td>207.832368</td>\n",
       "      <td>559.359350</td>\n",
       "      <td>0.075819</td>\n",
       "      <td>3.634086</td>\n",
       "      <td>14.908970</td>\n",
       "      <td>71.454424</td>\n",
       "      <td>5308.842566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "          count        mean         std       min         25%         50%  \\\n",
       "X         289.0  475.813149  254.277530  0.000000  300.000000  430.000000   \n",
       "Y         289.0  529.692042  300.895374  9.000000  269.000000  549.000000   \n",
       "Facies    289.0    0.813149    0.390468  0.000000    1.000000    1.000000   \n",
       "Porosity  289.0    0.134744    0.037745  0.058548    0.106318    0.126167   \n",
       "Perm      289.0  207.832368  559.359350  0.075819    3.634086   14.908970   \n",
       "\n",
       "                 75%          max  \n",
       "X         670.000000   990.000000  \n",
       "Y         819.000000   999.000000  \n",
       "Facies      1.000000     1.000000  \n",
       "Porosity    0.154220     0.228790  \n",
       "Perm       71.454424  5308.842566  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()                          # summary table of sand only DataFrame statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's transform the porosity and permeaiblity data to standard normal (mean = 0.0, standard deviation = 1.0, Gaussian shape). This is required for sequential Gaussian simulation (common target for our variogram models) and the Gaussian transform assists with outliers and provides more interpretable variograms. \n",
    "\n",
    "Let's look at the inputs for the GeostatsPy nscore program.  Note the output include an ndarray with the transformed values (in the same order as the input data in Dataframe 'df' and column 'vcol'), and the transformation table in original values and also in normal score values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.geostats.nscore(df, vcol, wcol=None, ismooth=False, dfsmooth=None, smcol=0, smwcol=0)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geostats.nscore                                         # see the input parameters required by the nscore function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following command will transform the Porosity and Permeabilty to standard normal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transform to Gaussian by Facies\n",
    "df['NPor'], tvPor, tnsPor = geostats.nscore(df, 'Porosity') # nscore transform for all facies porosity \n",
    "df['NPerm'], tvPermSand, tnsPermSand = geostats.nscore(df, 'Perm')  # nscore transform for all facies permeability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the updated DataFrame to make sure that we now have the normal score porosity and permeability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>NPor</th>\n",
       "      <th>NPerm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
       "      <td>0.115359</td>\n",
       "      <td>5.736104</td>\n",
       "      <td>-0.391400</td>\n",
       "      <td>-0.429142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "      <td>0.136425</td>\n",
       "      <td>17.211462</td>\n",
       "      <td>0.299307</td>\n",
       "      <td>0.112995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.135810</td>\n",
       "      <td>43.724752</td>\n",
       "      <td>0.272201</td>\n",
       "      <td>0.556521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>0.094414</td>\n",
       "      <td>1.609942</td>\n",
       "      <td>-1.269810</td>\n",
       "      <td>-1.178106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.113049</td>\n",
       "      <td>10.886001</td>\n",
       "      <td>-0.496733</td>\n",
       "      <td>-0.156762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     X    Y  Facies  Porosity       Perm      NPor     NPerm\n",
       "0  100  900       1  0.115359   5.736104 -0.391400 -0.429142\n",
       "1  100  800       1  0.136425  17.211462  0.299307  0.112995\n",
       "2  100  600       1  0.135810  43.724752  0.272201  0.556521\n",
       "3  100  500       0  0.094414   1.609942 -1.269810 -1.178106\n",
       "4  100  100       0  0.113049  10.886001 -0.496733 -0.156762"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()                                               # preview sand DataFrame with nscore transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks good! One way to check is to see if the relative magnitudes of the normal score transformed values match the original values.  e.g. that the normal score transform of 0.10 porosity normal score is less than the normal score transform of 0.14 porsity.  Also, the normal score transform of values close to the mean value should be close to 0.0 \n",
    "\n",
    "Let's also check the original and transformed sand and shale porosity distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)                                        # plot original sand and shale porosity histograms\n",
    "plt.hist(df['Porosity'], facecolor='red',bins=np.linspace(0.0,0.25,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label='Original')\n",
    "plt.xlim([0.05,0.25]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Porosity')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(222)  \n",
    "plt.hist(df['NPor'], facecolor='blue',bins=np.linspace(-3.0,3.0,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label = 'Trans')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Nscore Porosity')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(223)                                        # plot nscore transformed sand and shale histograms\n",
    "plt.hist(df['Perm'], facecolor='red',bins=np.linspace(0.0,1000.0,100000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label='Original')\n",
    "plt.xlim([0.0,1000.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Permeability')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(224)                                        # plot nscore transformed sand and shale histograms\n",
    "plt.hist(df['NPerm'], facecolor='blue',bins=np.linspace(-3.0,3.0,100000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label = 'Trans')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Permeability (mD)'); plt.ylabel('Frequency'); plt.title('Nscore Permeability')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=2.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The normal score transform has correctly transformed the porosity and permeability to standard normal.\n",
    "\n",
    "#### Method \\#1:Ocular Inspection of Posted Data\n",
    "\n",
    "Data visualization is very useful to detect patterns. Our brains are very good at pattern detection. I promote quantitative methods and recognize issues with cognitive bias, but it is important to recognize the value is expert intepretation based on data visualization.\n",
    "\n",
    "* This data visualization will also be important to assist with parameter selection for the quantitative methods later.\n",
    "\n",
    "Let's plot the location maps of normal score transforms of porosity and permeability for all facies. We will also include a cross plot of the nscore permeability vs. porosity colored by facies to aid with comparison in spatial features between the porosity and permeability data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.cm.plasma                    # color map\n",
    "plt.subplot(131)\n",
    "GSLIB.locmap_st(df,'X','Y','NPor',0,1000,0,1000,-3,3,'Nscore Porosity - All Facies','X (m)','Y (m)','Nscore Porosity',cmap)\n",
    "\n",
    "plt.subplot(132)\n",
    "GSLIB.locmap_st(df,'X','Y','NPerm',0,1000,0,1000,-3,3,'Nscore Permeability - All Facies','X (m)','Y (m)','Nscore Permeability',cmap)\n",
    "\n",
    "plt.subplot(133)\n",
    "facies = df['Facies'].values +0.01\n",
    "plt.scatter(df['NPor'],df['NPerm'],c = facies,edgecolor = 'black',cmap = plt.cm.inferno)\n",
    "#plt.plot([-3,3],[-3,3],color = 'black')\n",
    "plt.xlabel(r'Nscore Porosity')\n",
    "plt.ylabel(r'Nscore Permeability')\n",
    "plt.title('Nscore Permeability vs. Porosity')\n",
    "plt.xlim([-3,3])\n",
    "plt.ylim([-3,3])\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=0.8, wspace=0.5, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add the variogram map for irreuglar data.  I will add this to GeostatsPy soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do you see?  Here's my observations:\n",
    "\n",
    "* there is a high degree of spatial agreement between porosity and permeability, this is supported by the high correlation evident in the cross plot.\n",
    "* there are no discontinuities that could suggest that facies represent a distinct change, rather the porosity and permeability seem continuous and the assigned facies are a truncation of their continous behavoir, we doing 'ok' with no facies\n",
    "* suspect a 045 azimuth major direction of continuity (up - right)\n",
    "* there may be cycles in the 135 azimuth \n",
    "* there will not likely be a nugget effect, but there is an hint of some short scale discontinuity?\n",
    "\n",
    "**Do you agree?** If you have a different observations, drop me a line at mpyrcz@austin.utexas.edu and I'll add to this lesson with credit.\n",
    "\n",
    "#### Quantitative Methods\n",
    "\n",
    "Let's try out variogram maps. \n",
    "\n",
    "* I realized that I had not coded variogram maps in Python, so I just did it and added it to GeostatsPy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GSLIB's VARMAP program (Deutsch and Journel, 1998) converted from the original Fortran to Python \n",
    "# by Michael Pyrcz, the University of Texas at Austin (Jan, 2019)\n",
    "# Note simplified for 2D, irrelgular data only\n",
    "\n",
    "def varmapv(df,xcol,ycol,vcol,tmin,tmax,nxlag,nylag,dxlag,dylag,minnp,isill): \n",
    "\n",
    "# Parameters - consistent with original GSLIB    \n",
    "# df - DataFrame with the spatial data, xcol, ycol, vcol coordinates and property columns\n",
    "# tmin, tmax - property trimming limits\n",
    "# xlag, xltol - lag distance and lag distance tolerance\n",
    "# nlag - number of lags to calculate\n",
    "# azm, atol - azimuth and azimuth tolerance\n",
    "# bandwh - horizontal bandwidth / maximum distance offset orthogonal to azimuth\n",
    "# isill - 1 for standardize sill\n",
    "\n",
    "# Load the data\n",
    "\n",
    "    df_extract = df.loc[(df[vcol] >= tmin) & (df[vcol] <= tmax)]    # trim values outside tmin and tmax\n",
    "    nd = len(df_extract)\n",
    "    x = df_extract[xcol].values\n",
    "    y = df_extract[ycol].values\n",
    "    vr = df_extract[vcol].values\n",
    "    \n",
    "# Summary statistics for the data after trimming\n",
    "    avg = vr.mean()\n",
    "    stdev = vr.std()\n",
    "    sills = stdev**2.0\n",
    "    ssq = sills\n",
    "    vrmin = vr.min()\n",
    "    vrmax = vr.max()   \n",
    "    \n",
    "# Initialize the summation arrays\n",
    "    npp = np.zeros((nylag*2+1,nxlag*2+1))\n",
    "    gam = np.zeros((nylag*2+1,nxlag*2+1))\n",
    "    nppf = np.zeros((nylag*2+1,nxlag*2+1))\n",
    "    gamf = np.zeros((nylag*2+1,nxlag*2+1))\n",
    "    hm = np.zeros((nylag*2+1,nxlag*2+1))\n",
    "    tm = np.zeros((nylag*2+1,nxlag*2+1))\n",
    "    hv = np.zeros((nylag*2+1,nxlag*2+1))\n",
    "    tv = np.zeros((nylag*2+1,nxlag*2+1))\n",
    "\n",
    "# First fix the location of a seed point: \n",
    "    for i in range(0,nd):     \n",
    "# Second loop over the data: \n",
    "        for j in range(0,nd): \n",
    "# The lag:\n",
    "            ydis = y[j] - y[i]\n",
    "            iyl = nylag + int(ydis/dylag)\n",
    "            if iyl < 0 or iyl > nylag*2: # acocunting for 0,...,n-1 array indexing\n",
    "                continue\n",
    "            xdis = x[j] - x[i]\n",
    "            ixl = nxlag + int(xdis/dxlag)\n",
    "            if ixl < 0 or ixl > nxlag*2: # acocunting for 0,...,n-1 array indexing\n",
    "                continue\n",
    "                \n",
    "# We have an acceptable pair, therefore accumulate all the statistics\n",
    "# that are required for the variogram:\n",
    "            npp[iyl,ixl] = npp[iyl,ixl] + 1 # our ndarrays read from the base to top, so we flip\n",
    "            tm[iyl,ixl] = tm[iyl,ixl] + vr[i]\n",
    "            hm[iyl,ixl] = hm[iyl,ixl] + vr[j]\n",
    "            tv[iyl,ixl] = tm[iyl,ixl] + vr[i]*vr[i]\n",
    "            hv[iyl,ixl] = hm[iyl,ixl] + vr[j]*vr[j]\n",
    "            gam[iyl,ixl] = gam[iyl,ixl] + ((vr[i]-vr[j])*(vr[i]-vr[j]))\n",
    "\n",
    "# Get average values for gam, hm, tm, hv, and tv, then compute\n",
    "# the correct \"variogram\" measure:\n",
    "    for iy in range(0,nylag*2+1): \n",
    "        for ix in range(0,nxlag*2+1): \n",
    "            if npp[iy,ix] <= minnp:\n",
    "                gam[iy,ix] = -999.\n",
    "                hm[iy,ix]  = -999.\n",
    "                tm[iy,ix]  = -999.\n",
    "                hv[iy,ix]  = -999.\n",
    "                tv[iy,ix]  = -999.\n",
    "            else:\n",
    "                rnum = npp[iy,ix]\n",
    "                gam[iy,ix] = gam[iy,ix] / (2*rnum) # semivariogram\n",
    "                hm[iy,ix] = hm[iy,ix] / rnum\n",
    "                tm[iy,ix] = tm[iy,ix] / rnum\n",
    "                hv[iy,ix] = hv[iy,ix] / rnum - hm[iy,ix]*hm[iy,ix]\n",
    "                tv[iy,ix] = tv[iy,ix] / rnum - tm[iy,ix]*tm[iy,ix]\n",
    "                \n",
    "# Attempt to standardize:\n",
    "            if isill > 0:\n",
    "                gamf[iy,ix] = gamf[iy,ix]/sills\n",
    "\n",
    "    for iy in range(0,nylag*2+1): \n",
    "        for ix in range(0,nxlag*2+1):             \n",
    "            gamf[iy,ix] = gam[nylag*2-iy,ix]\n",
    "            nppf[iy,ix] = npp[nylag*2-iy,ix]\n",
    "            \n",
    "    return gamf, nppf    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The inputs include: \n",
    "\n",
    "* input data - DataFrame, 'df', and columns for x, y and property of interest, 'x', 'y' and 'vcol', \n",
    "* variogram map parameters - number of cells in each direction to search, 'nxlag', nylag', the cell size / lag distance, 'dxlag' and 'dylag'''\n",
    "* search - the minimum number of pairs reuqired to assign a result, 'mnpairs'\n",
    "* normalization - 1 for standardize variance to 1.0 and 0 for not\n",
    "\n",
    "The output is a 2D ndarray with the variogram map and the number of pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the output is (23, 23)\n"
     ]
    }
   ],
   "source": [
    "vmap, npmap = varmapv(df,'X','Y','NPor',tmin=-999,tmax=999,nxlag=11,nylag=11,dxlag=50,dylag=50,minnp=1,isill=1)\n",
    "\n",
    "xmin = -575;ymin = -575; xmax = 575; ymax = 575; step = 50; vmin = 0.0; vmax = 1.6 \n",
    "plt.subplot(111)\n",
    "xx, yy = np.meshgrid(np.arange(xmin, xmax, step), np.arange(ymax, ymin, -1 * step))\n",
    "#im = plt.contourf(xx,yy,vmap,cmap=cmap,vmin=vmin,vmax=vmax,levels=np.linspace(vmin, vmax, 100),)\n",
    "plt.imshow(vmap,interpolation = None,extent = [xmin,xmax,ymin,ymax], vmin = vmin, vmax = vmax,cmap = cmap)\n",
    "plt.title('Variogram Map'); plt.xlabel('X Offset (m)'); plt.ylabel('Y Offset (m)')\n",
    "cbar = plt.colorbar(im, orientation=\"vertical\", ticks=np.linspace(vmin, vmax, 10))\n",
    "cbar.set_label('Variogram Value', rotation=270, labelpad=20)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()\n",
    "\n",
    "print('The shape of the output is ' + str(vmap.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the output ndarray is 23, 23 cells? We asked for the number of cells to extend in each direction, 11 and 11 in x and y.  The map has an origin (zero distance) cell in the middle and extends 11 in both positive and negative directions.  So we have 2*$nx$ + 1, 2*$ny$ + 1 cells in the resulting variogram map and the $xmin = -1 * (nx * x_{cellsize} + \\frac{1}{2} x_{cellsize})$ and the $xmax = nx * x_{cellsize} + \\frac{1}{2} x_{cellsize}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do you think of this variogram map? These are my observations:\n",
    "\n",
    "* major continuity direction is at azimuth 045\n",
    "* there is a high degree of geometric anisotropy\n",
    "* there is cyclicity in the 135 direction \n",
    "* there may be some cyclicity in the 045 direction\n",
    "\n",
    "From this variogram map we can immediately see directionality in our spatial data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Experimental Variograms\n",
    "\n",
    "Another method for exploring spatial data directionality is the calculation of multiple experimental variograms for a variety of directions.\n",
    "\n",
    "We can use the location maps to help determine good variogram calculation parameters.\n",
    "\n",
    "```p\n",
    "tmin = -9999.; tmax = 9999.; \n",
    "lag_dist = 100.0; lag_tol = 50.0; nlag = 7; bandh = 9999.9; azi = azi; atol = 22.5; isill = 1\n",
    "```\n",
    "* tmin, tmax are trimming limits - set to have no impact, no need to filter the data\n",
    "* lag_dist, lag_tol are the lag distance, lag tolerance - set based on the common data spacing (100m) and tolerance as 100% of lag distance for additonal smoothing\n",
    "* nlag is number of lags - set to extend just past 50 of the data extent\n",
    "* bandh is the horizontal band width - set to have no effect\n",
    "* azi is the azimuth -  it has not effect since we set atol, the azimuth tolerance, to 90.0\n",
    "* isill is a boolean to standardize the distribution to a variance of 1 - it has no effect since the nscore transform sets the variance to 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmin = -9999.; tmax = 9999.                             # no trimming \n",
    "lag_dist = 100.0; lag_tol = 50.0; nlag = 7;            # maximum lag is 700m and tolerance > 1/2 lag distance for smoothing\n",
    "bandh = 9999.9; atol = 22.5                             # no bandwidth, directional variograms\n",
    "isill = 1                                               # standardize sill\n",
    "azi_mat = [0,22.5,45,67.5,90,112.5,135,157.5]           # directions in azimuth to consider"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try running these variograms and visualizing them on separate plots.  I'll demonstrate a method to promgramtically loop over each direction for efficiency (and code brevity).\n",
    "\n",
    "* we have the direction in the list called 'azi_mat'\n",
    "* we use the command:\n",
    "```p\n",
    "for iazi in range(0,len(azi_mat)): \n",
    "```\n",
    "to loop over all the elements in the list with index 'iazi'\n",
    "\n",
    "* we run the variogram calculation and store the reuslts in 2D arrays, iy is direction, ix is the lag\n",
    "* we use subplots with the 'iazi' index to add each plot\n",
    "\n",
    "```p\n",
    "    plt.subplot(4,2,iazi+1)\n",
    "```\n",
    "we add one because the plot index must be $1,\\ldots,n$, but arrays / list index as $0,\\ldots,n-1$ in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Arrays to store the results\n",
    "lag = np.zeros((len(azi_mat),nlag+2)); gamma = np.zeros((len(azi_mat),nlag+2)); npp = np.zeros((len(azi_mat),nlag+2));\n",
    "\n",
    "for iazi in range(0,len(azi_mat)):                      # Loop over all directions\n",
    "    lag[iazi,:], gamma[iazi,:], npp[iazi,:] = geostats.gamv(df,\"X\",\"Y\",\"NPor\",tmin,tmax,lag_dist,lag_tol,nlag,azi_mat[iazi],atol,bandh,isill)\n",
    "    plt.subplot(4,2,iazi+1)\n",
    "    plt.plot(lag[iazi,:],gamma[iazi,:],'x',color = 'black',label = 'Azimuth ' +str(azi_mat[iazi]))\n",
    "    plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "    plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    plt.title('Directional NSCORE Porosity Variogram')\n",
    "    plt.xlim([0,700])\n",
    "    plt.ylim([0,1.8])\n",
    "    plt.legend(loc='upper left')\n",
    "    plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=4.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
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   "source": [
    "The directional variograms provide a very clear image of directionality. The ranges vary from 300m, 500m to zonal anisotropy, to 500m, 350m, 280m, 250m and finally 280m.  We are observing the actually spatial continuity ellipse by exploring a variety of directions! \n",
    "\n",
    "* We can observe that Azimuth 045 is the major direction and Azimuth 135 is the minor direction.\n",
    "\n",
    "This is a very powerful tool for exploring directionality in spatial datasets."
   ]
  },
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   "source": [
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of spatial data directional continuity analysis. Much more could be done, I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)"
   ]
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